The performance of four different dispatching and rebalancing algorithms for the control of an automated mobility-on-demand system is evaluated in a simulation environment. The case study conducted with an agent-based simulation scenario of the city of Zurich shows that the choice of an intelligent rebalancing algorithm decreases the average wait time in the system. For a wait time of four minutes at peak hours the most performant algorithm requires the same price per vehicle kilometer as a private car today. The results show that such an automated mobility on demand service can be offered while maintaining a higher fleet occupancy than with conventional private carsShow more